These days, all the main operators and third parties responsible for the network in the Telecom sector run both preventive maintenance and corrective maintenance models. If you get the right information at the right time, the former—and more disruptive—model can break old trade-offs, and assure lower operation costs and higher network stability to not only electric systems, but also business intelligence systems. In doing so, it also allows efforts and investment to be focused on innovation developments.
The predictive maintenance approach measures historical and real-time data from the Network Elements to understand the process of service degradation before failure. It also predicts which Network Elements are more likely to fail in the upcoming days or hours, using predictive analytics tools and techniques.
It can, therefore, work as a periodic inspection of the equipment’s conditions, but in an automatic and self-learning way—determining in advance the need for maintenance services, increasing the equipment’s availability time, and reducing the amount of unplanned emergency work. It can also increase the confidence level in equipment performance by predicting the probability of failure, as well as the useful lifetime expectancy of the equipment and the conditions needed for maximizing that time. To do that, predictive maintenance demands a solid information platform and network reading systems.
Predictive maintenance can break the trade-offs of the older strategies by enabling telecommunication companies to maximize the useful life of their equipment while avoiding unplanned downtime, minimizing planned downtime, and saving costs.
In essence, predictive maintenance is a disruptive innovation because, in addition to the operating benefits, it also helps the company to act in regions with high client churn rates, and where they suffer with continued falls in the network.
Contact Person: Mr. Arms Wang